2020

1. JEBO

   Explaining escalating prices and fines: A unified approach

   Stefan Buehler, and Nicolas Eschenbaum

   Journal of Economic Behavior & Organization, 2020

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   This paper provides an explanation for escalating prices and fines based on a unified analytical framework that nests monopoly pricing and optimal law enforcement. We show that escalation emerges as an optimal outcome if the principal (i) lacks commitment ability, and (ii) gives less than full weight to agent benefits. Escalation is driven by decreasing transfers for non-active agents rather than increasing transfers for active agents. Some forward-looking agents then strategically delay their activity, which drives a wedge between the optimal static transfer and the benefit of an indifferent agent. This wedge is the source of escalation.

   @article{buehler-eschenbaum-2020,
     author = {Buehler, Stefan and Eschenbaum, Nicolas},
     journal = {Journal of Economic Behavior & Organization},
     title = {Explaining escalating prices and fines: A unified approach},
     year = {2020},
     issn = {0167-2681},
     pages = {153 - 164},
     volume = {171},
     doi = {https://doi.org/10.1016/j.jebo.2020.01.008},
     keywords = {Escalation, Behavior-based pricing, Repeat offenders, Deterrence},
     url = {http://www.sciencedirect.com/science/article/pii/S0167268120300093},
   }

2021

1. arXiv

   Dealing with Uncertainty: The Value of Reputation in the Absence of Legal Institutions

   Nicolas Eschenbaum, and Helge Liebert

   May 2021

   Abs arXiv Bib PDF

   This paper studies reputation in the online market for illegal drugs in which no legal institutions exist to alleviate uncertainty. Trade takes place on platforms that offer rating systems for sellers, thereby providing an observable measure of reputation. The analysis exploits the fact that one of the two dominant platforms unexpectedly disappeared. Re-entering sellers reset their rating. The results show that on average prices decreased by up to 9% and that a 1% increase in rating causes a price increase of 1%. Ratings and prices recover after about three months. We calculate that identified good types earn 1,650 USD more per week.

   @unpublished{eschenbaum-liebert-2021,
     author = {Eschenbaum, Nicolas and Liebert, Helge},
     title = {Dealing with Uncertainty: The Value of Reputation in the Absence of Legal Institutions},
     month = may,
     year = {2021},
     keywords = {Reputation, institutions, uncertainty, dark web, drugs},
   }

2. arXiv

   Dynamic Monopoly Pricing with Multiple Varieties: Trading Up

   Stefan Buehler, and Nicolas Eschenbaum

   Dec 2021

   Abs arXiv Bib PDF

   This paper studies dynamic monopoly pricing for a class of settings that includes multiple durable, multiple rental, or a mix of varieties. We show that the driving force behind pricing dynamics is the seller’s incentive to switch consumers - buyers and non-buyers - to higher-valued consumption options by lowering prices ("trading up"). If consumers cannot be traded up from the static optimal allocation, pricing dynamics do not emerge in equilibrium. If consumers can be traded up, pricing dynamics arise until all trading-up opportunities are exhausted. We study the conditions under which pricing dynamics end in finite time and characterize the final prices at which dynamics end.

   @unpublished{buehler-eschenbaum-2021,
     author = {Buehler, Stefan and Eschenbaum, Nicolas},
     title = {Dynamic Monopoly Pricing with Multiple Varieties: Trading Up},
     month = dec,
     year = {2021},
   }

3. arXiv

   Robust Algorithmic Collusion

   Nicolas Eschenbaum, Filip Mellgren, and Philipp Zahn

   Dec 2021

   Abs arXiv Bib PDF

   This paper develops a formal framework to assess policies of learning algorithms in economic games. We investigate whether reinforcement-learning agents with collusive pricing policies can successfully extrapolate collusive behavior from training to the market. We find that in testing environments collusion consistently breaks down. Instead, we observe static Nash play. We then show that restricting algorithms’ strategy space can make algorithmic collusion robust, because it limits overfitting to rival strategies. Our findings suggest that policy-makers should focus on firm behavior aimed at coordinating algorithm design in order to make collusive policies robust

   @unpublished{eschenbaum-mellgren-zahn-2021,
     author = {Eschenbaum, Nicolas and Mellgren, Filip and Zahn, Philipp},
     title = {Robust Algorithmic Collusion},
     month = dec,
     year = {2021},
   }

4. WP

   Experimental Evidence on Coasian Dynamics and the Ratchet Effect

   Stefan Buehler, Thomas Epper, Nicolas Eschenbaum, and 1 more author

   Jul 2021

2018

1. WP

   Estimating Geographic Market Size Nonparametrically: An Application to Grocery Retailing

   Nicolas Eschenbaum

   Nov 2018

   Abs PDF

   This paper develops a nonparametric approach to empirically determine geographic market size. I exploit highly detailed spatial data and provide estimates of business-stealing effects across distance by studying the impact of store entry on competitors in an increasing range to the entry site. Entropy balancing is employed to control for systematic differences across local markets. I estimate that markets for Swiss grocery retailing stores are highly localized in a tight four kilometer radius. I further document evidence that the impact weakens with increasing distance and that smaller retailers compete in a more narrow market of only two kilometers in size.